Event Name: Graduate Seminar Series: Clinical Stream
Graduate Seminar Series for the Institute of Biomaterials and Biomedical Engineering (IBBME). This day is for clinical stream presenters.
Location: Red Seminar Room – Donnelly Building
Presentation Title: Simulating and Compensating for Bioelectric Signal Drift in Chronically Implanted Nerve Cuff Electrodes
Abstract: Recordings from the peripheral nervous system can be used to extract command signals in a variety of neuroprosthetic and neuromodulation applications. These signals can be acquired using intraneural (nerve penetrating) or extraneural (non-nerve penetrating) electrodes. While intraneural electrodes provide better signal to noise ratios, they are more likely to cause damage to the nerve and therefore may not be optimal for long-term use. Extraneural electrodes, such as nerve cuff electrodes, are known to be safe for chronic implantation, however, they provide poorer recording selectivity (i.e., the ability to discriminate signals from different neural sources). New signal processing approaches are needed to improve the recording selectivity of nerve cuff recordings. Our group has recently introduced novel signal processing methods and validated them using simulation and acute in vivo studies in a rat model. Before these methods can be translated to humans, their performance in chronic implantation scenarios must be optimized. By characterizing the changes seen in recordings from chronically implanted nerve cuff electrodes, we can test and investigate the efficacy of current and novel methods that attempt to improve upon the recording selectivity of these devices. The purpose of this research is to simulate the changes observed over time in recordings from nerve cuff electrodes and to develop appropriate strategies (i.e., adaptive selective recording algorithms) capable of compensating for these changes. These objectives will be achieved by modifying previously developed computational models of a rat sciatic nerve, to simulate factors likely to cause bioelectric signal drift, and therefore reduce selectivity, in nerve cuff electrode recordings. After characterizing the changes that are expected to occur over time, we will simulate and compare several approaches for maintaining high levels of performance during chronic implantation of extraneural electrodes. A previously developed selective recording approach, consisting of a classifier for discriminating compound action potentials originating from different neural pathways, will be used for this evaluation. The ability to incorporate these adaptive algorithms into peripheral nerve interfaces would provide improved control signals for neuroprosthetic applications and result in substantially more effective assistive technologies for a variety of clinical populations, including amputees and individuals living with spinal cord injury.
Supervisor Name: Jose Zariffa
Year of Study: 2
Program of Study: MASc
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